Objectives
To compare changes in preferences for life‐sustaining treatments (LSTs) and subsequent mortality of younger and older inpatients.
Design
Retrospective cohort study.
Setting
Kaiser ...Permanente Northern California (KPNC).
Participants
Individuals hospitalized at 21 KPNC hospitals between 2008 and 2012 (N = 227,525).
Measurements
Participants were divided according to age (<65, 65–84, ≥85). The effect of age on adding new and reversing prior LST limitations was evaluated. Survival to inpatient discharge was compared according to age group after adding new LST limitations.
Results
At admission, 18,254 (54.2%) of those aged 85 and older, 18,349 (20.8%) of those aged 65 to 84, and 3,258 (3.1%) of those younger than 65 had requested that the use of LST be limited. Of the 187,664 participants who initially did not request limitations on the use of LST, 15,932 (8.5%) had new LST limitations added; of the 39,861 admitted with LST limitations, 3,017 (7.6%) had these reversed. New limitations were more likely to be seen in older participants (aged 65–84, odds ratio (OR) = 2.27, 95% confidence interval (CI) = 2.16–2.39; aged ≥85, OR = 6.43, 95% CI = 6.05–6.84), and reversals of prior limitations were less likely to be seen in older individuals (aged 65–84, OR = 0.73, 95% CI = 0.65–0.83; aged ≥85, OR = 0.46, 95% CI = 0.41–0.53) than in those younger than 65. Survival rates to inpatient discharge were 71.7% of subjects aged 85 and older who added new limitations, 57.2% of those aged 65 to 84, and 43.4% of those younger than 65 (P < .001).
Conclusion
Changes in preferences for LSTs were common in hospitalized individuals. Age was an important determinant of likelihood of adding new or reversing prior LST limitations. Of subjects who added LST limitations, those who were older were more likely than those who were younger to survive to hospital discharge.
Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients' rehospitalization risk. Understanding a patient's readmission risk may ...help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone.
We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR-derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman's rho statistic. For all models, the results reported were obtained after fivefold cross validation.
The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors).
In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Risk adjustment is essential in evaluating the performance of an ICU; however, assigning scores is time-consuming. We sought to create an automated ICU risk adjustment score, based on the Simplified ...Acute Physiology Score 3, using only data available within the electronic medical record (Kaiser Permanente HealthConnect).
The eSimplified Acute Physiology Score 3 was developed by adapting Kaiser Permanente HealthConnect structured data to Simplified Acute Physiology Score 3 criteria. The model was tested among 67,889 first-time ICU admissions at 21 hospitals between 2007 and 2011 to predict hospital mortality. Model performance was evaluated using published Simplified Acute Physiology Score 3 global and North American coefficients; a first-level customized version of the eSimplified Acute Physiology Score 3 was also developed in a 40% derivation cohort and tested in a 60% validation cohort.
Electronic variables were considered "directly" available if they could be mapped exactly within Kaiser Permanente HealthConnect; they were considered "adapted" if no exact electronic corollary was identified. Model discrimination was evaluated with area under receiver operating characteristic curves; calibration was assessed using Hosmer-Lemeshow goodness-of-fit tests.
Mean age at ICU admission was 65 ± 17 yrs. Mortality in the ICU was 6.2%; total in-hospital mortality was 11.2%. The majority of Simplified Acute Physiology Score 3 variables were considered "directly" available; others required adaptation based on diagnosis coding, medication records, or procedure tables. Mean eSimplified Acute Physiology Score 3 scores were 45 ± 13. Using published Simplified Acute Physiology Score 3 global and North American coefficients, the eSimplified Acute Physiology Score 3 demonstrated good discrimination (area under the receiver operating characteristic curve, 0.80-0.81); however, it overpredicted mortality. The customized eSimplified Acute Physiology Score 3 score demonstrated good discrimination (area under the receiver operating characteristic curve, 0.82) and calibration (Hosmer-Lemeshow goodness-of-fit chi-square p = 0.57) in the validation cohort. The eSimplified Acute Physiology Score 3 demonstrated stable performance when cohorts were limited to specific hospitals and years.
The customized eSimplified Acute Physiology Score 3 shows good potential for providing automated risk adjustment in the intensive care unit.
ObjectiveTo examine the value of health systems data as indicators of emerging COVID-19 activity.DesignObservational study of health system indicators for the COVID Hotspotting Score (CHOTS) with ...prospective validation.Setting and participantsAn integrated healthcare delivery system in Northern California including 21 hospitals and 4.5 million members.Main outcome measuresThe CHOTS incorporated 10 variables including four major (cough/cold calls, emails, new positive COVID-19 tests, COVID-19 hospital census) and six minor (COVID-19 calls, respiratory infection and COVID-19 routine and urgent visits, and respiratory viral testing) indicators assessed with change point detection and slope metrics. We quantified cross-correlations lagged by 7–42 days between CHOTS and standardised COVID-19 hospital census using observational data from 1 April to 31 May 2020 and two waves of prospective data through 21 March 2021.ResultsThrough 30 September 2020, peak cross-correlation between CHOTS and COVID-19 hospital census occurred with a 28-day lag at 0.78; at 42 days, the correlation was 0.69. Lagged correlation between medical centre CHOTS and their COVID-19 census was highest at 42 days for one facility (0.63), at 35 days for nine facilities (0.52–0.73), at 28 days for eight facilities (0.28–0.74) and at 14 days for two facilities (0.73–0.78). The strongest correlation for individual indicators was 0.94 (COVID-19 census) and 0.90 (new positive COVID-19 tests) lagged 1–14 days and 0.83 for COVID-19 calls and urgent clinic visits lagged 14–28 days. Cross-correlation was similar (0.73) with a 35-day lag using prospective validation from 1 October 2020 to 21 March 2021.ConclusionsPassively collected health system indicators were strongly correlated with forthcoming COVID-19 hospital census up to 6 weeks before three successive COVID-19 waves. These tools could inform communities, health systems and public health officials to identify, prepare for and mitigate emerging COVID-19 activity.
Background:
A comorbidity summary score may support early and systematic identification of women at high risk for adverse obstetric outcomes. The objective of this study was to conduct the initial ...development and validation of an obstetrics comorbidity risk score for automated implementation in the electronic health record (EHR) for clinical use.
Methods:
The score was developed and validated using EHR data for a retrospective cohort of pregnancies with delivery between 2010 and 2018 at Kaiser Permanente Northern California, an integrated health care system. The outcome used for model development consisted of adverse obstetric events from delivery hospitalization (
e.g.
, eclampsia, hemorrhage, death). Candidate predictors included maternal age, parity, multiple gestation, and any maternal diagnoses assigned in health care encounters in the 12 months before admission for delivery. We used penalized regression for variable selection, logistic regression to fit the model, and internal validation for model evaluation. We also evaluated prenatal model performance at 18 weeks of pregnancy.
Results:
The development cohort (
n
= 227,405 pregnancies) had an outcome rate of 3.8% and the validation cohort (
n
= 41,683) had an outcome rate of 2.9%. Of 276 candidate predictors, 37 were included in the final model. The final model had a validation c-statistic of 0.72 (95% confidence interval CI 0.70–0.73). When evaluated at 18 weeks of pregnancy, discrimination was modestly diminished (c-statistic 0.68 95% CI 0.67–0.70).
Conclusions:
The obstetric comorbidity score demonstrated good discrimination for adverse obstetric outcomes. After additional appropriate validation, the score can be automated in the EHR to support early identification of high-risk women and assist efforts to ensure risk-appropriate maternal care.
Sepsis survivors face increased risk for cardiovascular complications; however, the contribution of intrasepsis events to cardiovascular risk profiles is unclear.
Kaiser Permanente Northern ...California (KPNC) and Intermountain Healthcare (IH) integrated healthcare delivery systems.
Sepsis survivors (2011-2017 KPNC and 2018-2020 IH) greater than or equal to 40 years old without prior cardiovascular disease.
Data across KPNC and IH were harmonized and grouped into presepsis (demographics, atherosclerotic cardiovascular disease scores, comorbidities) or intrasepsis factors (e.g., laboratory values, vital signs, organ support, infection source) with random split for training/internal validation datasets (75%/25%) within KPNC and IH. Models were bidirectionally, externally validated between healthcare systems.
None.
Changes to predictive accuracy (
-statistic) of cause-specific proportional hazards models predicting 1-year cardiovascular outcomes (atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation events) were compared between models that did and did not contain intrasepsis factors. Among 39,590 KPNC and 16,388 IH sepsis survivors, 3,503 (8.8%) at Kaiser Permanente (KP) and 600 (3.7%) at IH experienced a cardiovascular event within 1-year after hospital discharge, including 996 (2.5%) at KP and 192 (1.2%) IH with an atherosclerotic event first, 564 (1.4%) at KP and 117 (0.7%) IH with a heart failure event, 2,310 (5.8%) at KP and 371 (2.3%) with an atrial fibrillation event. Death within 1 year after sepsis occurred for 7,948 (20%) KP and 2,085 (12.7%) IH patients. Combined models with presepsis and intrasepsis factors had better discrimination for cardiovascular events (KPNC
-statistic 0.783 95% CI, 0.766-0.799; IH 0.763 0.726-0.801) as compared with presepsis cardiovascular risk alone (KPNC: 0.666 0.648-0.683, IH 0.660 0.619-0.702) during internal validation. External validation of models across healthcare systems showed similar performance (KPNC model within IH data
-statistic: 0.734 0.725-0.744; IH model within KPNC data: 0.787 0.768-0.805).
Across two large healthcare systems, intrasepsis factors improved postsepsis cardiovascular risk prediction as compared with presepsis cardiovascular risk profiles. Further exploration of sepsis factors that contribute to postsepsis cardiovascular events is warranted for improved mechanistic and predictive models.
The authors used a validated model with electronic-medical-record data to identify hospitalized patients at high risk for clinical deterioration. The intervention, which involved remote monitoring by ...nurses who reviewed records of high-risk patients and communicated with in-hospital rapid-response teams, was associated with decreased 30-day mortality.
Objective:
To evaluate whether COVID-19 vaccination status or mode of anesthesia modified the temporal harms associated with surgery following coronavirus disease-2019 (COVID-19) infection.
...Background:
Surgery shortly after COVID-19 infection is associated with higher rates of complications, leading to recommendations to delay surgery following COVID-19 infection when possible. However, prior studies were based on populations with low or no prevalence of vaccination.
Methods:
A retrospective cohort study of patients who underwent scheduled surgery in a health system from January 1, 2020 to February 28, 2022 (N=228,913) was performed. Patients were grouped by time of surgery relative to COVID-19 test positivity: 0 to 4 weeks after COVID-19 (“early post-COVID-19”), 4 to 8 weeks after COVID-19 (“mid post-COVID-19”), >8 weeks after COVID-19 (“late post-COVID-19”), surgery at least 30 days before subsequent COVID-19 (“pre-COVID-19”), and surgery with no prior or subsequent test positivity for COVID-19.
Results:
Among patients who were not fully vaccinated at the time of COVID-19 infection, the adjusted rate of perioperative complications for the early post-COVID-19 group was significantly higher than for the pre-COVID-19 group (relative risk: 1.55;
P
=0.05). No significantly higher risk was identified between these groups for patients who were fully vaccinated (0.66;
P
=1.00), or for patients who were not fully vaccinated and underwent surgery without general anesthesia (0.52;
P
=0.83).
Conclusions:
Surgery shortly following COVID-19 infection was not associated with higher risks among fully vaccinated patients or among patients who underwent surgery without general anesthesia. Further research will be valuable to understand additional factors that modify perioperative risks associated with prior COVID-19 infection.
IMPORTANCE: Current algorithms for management of neonatal early-onset sepsis (EOS) result in medical intervention for large numbers of uninfected infants. We developed multivariable prediction models ...for estimating the risk of EOS among late preterm and term infants based on objective data available at birth and the newborn’s clinical status. OBJECTIVES: To examine the effect of neonatal EOS risk prediction models on sepsis evaluations and antibiotic use and assess their safety in a large integrated health care system. DESIGN, SETTING, AND PARTICIPANTS: The study cohort includes 204 485 infants born at 35 weeks’ gestation or later at a Kaiser Permanente Northern California hospital from January 1, 2010, through December 31, 2015. The study compared 3 periods when EOS management was based on (1) national recommended guidelines (baseline period January 1, 2010, through November 31, 2012), (2) multivariable estimates of sepsis risk at birth (learning period December 1, 2012, through June 30, 2014), and (3) the multivariable risk estimate combined with the infant’s clinical condition in the first 24 hours after birth (EOS calculator period July 1, 2014, through December 31, 2015). MAIN OUTCOMES AND MEASURES: The primary outcome was antibiotic administration in the first 24 hours. Secondary outcomes included blood culture use, antibiotic administration between 24 and 72 hours, clinical outcomes, and readmissions for EOS. RESULTS: The study cohort included 204 485 infants born at 35 weeks’ gestation or later: 95 343 in the baseline period (mean SD age, 39.4 1.3 weeks; 46 651 male 51.0%; 37 007 white, non-Hispanic 38.8%), 52 881 in the learning period (mean SD age, 39.3 1.3 weeks; 27 067 male 51.2%; 20 175 white, non-Hispanic 38.2%), and 56 261 in the EOS calculator period (mean SD age, 39.4 1.3 weeks; 28 575 male 50.8%; 20 484 white, non-Hispanic 36.4%). In a comparison of the baseline period with the EOS calculator period, blood culture use decreased from 14.5% to 4.9% (adjusted difference, −7.7%; 95% CI, −13.1% to −2.4%). Empirical antibiotic administration in the first 24 hours decreased from 5.0% to 2.6% (adjusted difference, −1.8; 95% CI, −2.4% to −1.3%). No increase in antibiotic use occurred between 24 and 72 hours after birth; use decreased from 0.5% to 0.4% (adjusted difference, 0.0%; 95% CI, −0.1% to 0.2%). The incidence of culture-confirmed EOS was similar during the 3 periods (0.3% in the baseline period, 0.3% in the learning period, and 0.2% in the EOS calculator period). Readmissions for EOS (within 7 days of birth) were rare in all periods (5.2 per 100 000 births in the baseline period, 1.9 per 100 000 births in the learning period, and 5.3 per 100 000 births in the EOS calculator period) and did not differ statistically (P = .70). Incidence of adverse clinical outcomes, including need for inotropes, mechanical ventilation, meningitis, and death, was unchanged after introduction of the EOS calculator. CONCLUSIONS AND RELEVANCE: Clinical care algorithms based on individual infant estimates of EOS risk derived from a multivariable risk prediction model reduced the proportion of newborns undergoing laboratory testing and receiving empirical antibiotic treatment without apparent adverse effects.
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